{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bikeshare数据集上的特征工程\n",
    "\n",
    "    任务描述 请在Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的自行车数据上进行回归分析。根据每天的天气信息，预测该天的单车共享骑行量。\n",
    " \n",
    "    字段说明\n",
    "        Instant记录号 \n",
    "        Dteday：日期 \n",
    "        Season：季节（1=春天、2=夏天、3=秋天、4=冬天）\n",
    "        yr：年份，(0: 2011, 1:2012) \n",
    "        mnth：月份( 1 to 12) \n",
    "        hr：小时 (0 to 23) （只在hour.csv有，作业忽略此字段） \n",
    "        holiday：是否是节假日 weekday：星期中的哪天，取值为0～6 \n",
    "        workingday：是否工作日 1=工作日 （是否为工作日，1为工作日，0为非周末或节假日 \n",
    "        weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾） \n",
    "        temp：气温摄氏度 \n",
    "        atemp：体感温度\n",
    "        hum：湿度 \n",
    "        windspeed：风速 \n",
    "        casual：非注册用户个数\n",
    "        registered：注册用户个数 \n",
    "        cnt：给定日期（天）时间（每小时）总租车人数，响应变量y （cnt = casual + registered）\n",
    "        casual、registered和cnt三个特征均为要预测的y，作业里只需对cnt进行预测\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "weekday属性的不同取值和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "feature = ['season','mnth','weathersit','weekday']\n",
    "for col in feature:\n",
    "    print ('\\n%s属性的不同取值和出现的次数'%col)\n",
    "    print (train[col].value_counts())\n",
    "    train[col] = train[col].astype('object')\n",
    "    train[col] = train[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6    ...      weathersit_1  weathersit_2  weathersit_3  \\\n",
       "0       0       0    ...                 0             1             0   \n",
       "1       0       0    ...                 0             1             0   \n",
       "2       0       0    ...                 1             0             0   \n",
       "3       0       0    ...                 1             0             0   \n",
       "4       0       0    ...                 1             0             0   \n",
       "\n",
       "   weekday_0  weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          0          0          1  \n",
       "1          1          0          0          0          0          0          0  \n",
       "2          0          1          0          0          0          0          0  \n",
       "3          0          0          1          0          0          0          0  \n",
       "4          0          0          0          1          0          0          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['season','mnth','weathersit','weekday']\n",
    "x_train_cat = train[features]\n",
    "x_train_cat = pd.get_dummies(x_train_cat)\n",
    "x_train_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zjkf\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.cnt.values, bins=30, kde=True)\n",
    "plt.xlabel('Median value of peoples', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of peoples')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(train.shape[0]), train[\"cnt\"].values,color='purple')\n",
    "plt.title(\"Distribution of peoples\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
